This work can be further improved in two directions. First, although adding physics can help stabilize the training process, GAN is still hard to train due to the min-max game. One possible solution is to use the normalizing flow [1]} as the generator, which can explicitly compute the data likelihood and thus the discriminator is not needed. Second, the proposed model needs to be re-trained if applied to other roads or to the same road but within a new time slot, which limits the real-world application of the proposed model. Thus, the model generalizability for unseen scenarios is worth investigating.
